View article
Palette: Image-to-Image Diffusion Models Open
This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpainting, uncropping, an…
View article
End-to-end Optimized Image Compression Open
We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation. The transforms are constructed in three successive stages of convolutional linear fi…
View article
Deep Joint Source-Channel Coding for Wireless Image Transmission Open
We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-…
View article
Joint Autoregressive and Hierarchical Priors for Learned Image\n Compression Open
Recent models for learned image compression are based on autoencoders,\nlearning approximately invertible mappings from pixels to a quantized latent\nrepresentation. These are combined with an entropy model, a prior on the latent\nrepresen…
View article
Reversible data hiding: Advances in the past two decades Open
In the past two decades, reversible data hiding (RDH), also referred to as lossless or invertible data hiding, has gradually become a very active research area in the field of data hiding. This has been verified by more and more papers on …
View article
Countering Adversarial Images using Input Transformations Open
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Specifically, we study applying image transformations such as …
View article
Joint Autoregressive and Hierarchical Priors for Learned Image Compression Open
Recent models for learned image compression are based on autoencoders, learning approximately invertible mappings from pixels to a quantized latent representation. These are combined with an entropy model, a prior on the latent representat…
View article
End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling Open
This article proposes an end-to-end learnt lossy image compression approach, which is built on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure with Non-Local Attention optimization and Improved Context m…
View article
Lossy Image Compression with Compressive Autoencoders Open
We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algo- rithms …
View article
Lossy Image Compression with Compressive Autoencoders Open
We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms wh…
View article
DeepISP: Toward Learning an End-to-End Image Processing Pipeline Open
We present DeepISP, a full end-to-end deep neural model of the camera image signal processing (ISP) pipeline. Our model learns a mapping from the raw low-light mosaiced image to the final visually compelling image and encompasses low-level…
View article
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression Open
Deep neural networks (DNNs) have achieved great success in solving a variety of machine learning (ML) problems, especially in the domain of image recognition. However, recent research showed that DNNs can be highly vulnerable to adversaria…
View article
Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework Open
Adoption of deep learning in image steganalysis is still in its initial stage. In this paper we propose a generic hybrid deep-learning framework for JPEG steganalysis incorporating the domain knowledge behind rich steganalytic models. Our …
View article
MemNet: A Persistent Memory Network for Image Restoration Open
Recently, very deep convolutional neural networks (CNNs) have been attracting considerable attention in image restoration. However, as the depth grows, the long-term dependency problem is rarely realized for these very deep models, which r…
View article
JPEG Pleno: Toward an Efficient Representation of Visual Reality Open
S.14-20
View article
Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images Open
The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interes…
View article
Double JPEG compression forensics based on a convolutional neural network Open
Double JPEG compression detection has received considerable attention in blind image forensics. However, only few techniques can provide automatic localization. To address this challenge, this paper proposes a double JPEG compression detec…
View article
Implementing the DICOM Standard for Digital Pathology Open
Implementation of DICOM allows efficient access to image data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pa…
View article
The ALASKA Steganalysis Challenge Open
International audience
View article
Compression Artifacts Removal Using Convolutional Neural Networks Open
This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previousl…
View article
A comprehensive study of the rate-distortion performance in MPEG point cloud compression Open
Recent trends in multimedia technologies indicate the need for richer imaging modalities to increase user engagement with the content. Among other alternatives, point clouds denote a viable solution that offers an immersive content represe…
View article
CAS-CNN: A deep convolutional neural network for image compression artifact suppression Open
Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user expe…
View article
Real-Time Adaptive Image Compression Open
We present a machine learning-based approach to lossy image compression which outperforms all existing codecs, while running in real-time. Our algorithm typically produces files 2.5 times smaller than JPEG and JPEG 2000, 2 times smaller th…
View article
NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images Open
This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3channel RGB image. The challenge was divided into 2 tracks: the "Clean" track…
View article
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single\n Image Super-Resolution and Beyond Open
Single image super-resolution (SISR) deals with a fundamental problem of\nupsampling a low-resolution (LR) image to its high-resolution (HR) version.\nLast few years have witnessed impressive progress propelled by deep learning\nmethods. H…
View article
NTIRE 2022 Spectral Recovery Challenge and Data Set Open
This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the ARAD 1K data set: a new, l…
View article
Vision Transformers in Image Restoration: A Survey Open
The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated in most computer vision tasks. Now, both CNN and ViT are efficient approaches that…
View article
A Recursive Reversible Data Hiding in Encrypted Images Method With a Very High Payload Open
Reversible data hiding in encrypted images (RDHEI) can be used as an effective technique to embed additional data directly in the encrypted domain and therefore, without any invasion to privacy. In this way, RDHEI is especially useful for …
View article
JPEG XL next-generation image compression architecture and coding tools Open
An update on the JPEG XL standardization effort: JPEG XL is a practical approach focused on scalable web distribution and efficient compression of high-quality images. It will provide various benefits compared to existing image formats: si…
View article
JPEG Quantization Step Estimation and Its Applications to Digital Image Forensics Open
International audience